Advanced techniques in reliability model representation and solution

The current tendency of flight control system designs is towards increased integration of applications and increased distribution of computational elements. The reliability analysis of such systems is difficult because subsystem interactions are increasingly interdependent. Researchers at NASA Langley Research Center have been working for several years to extend the capability of Markov modeling techniques to address these problems. This effort has been focused in the areas of increased model abstraction and increased computational capability. The reliability model generator (RMG) is a software tool that uses as input a graphical object-oriented block diagram of the system. RMG uses a failure-effects algorithm to produce the reliability model from the graphical description. The ASSURE software tool is a parallel processing program that uses the semi-Markov unreliability range evaluator (SURE) solution technique and the abstract semi-Markov specification interface to the SURE tool (ASSIST) modeling language. A failure modes-effects simulation is used by ASSURE. These tools were used to analyze a significant portion of a complex flight control system. The successful combination of the power of graphical representation, automated model generation, and parallel computation leads to the conclusion that distributed fault-tolerant system architectures can now be analyzed

User's guide to the Reliability Estimation System Testbed (REST)

The Reliability Estimation System Testbed is an X-window based reliability modeling tool that was created to explore the use of the Reliability Modeling Language (RML). RML was defined to support several reliability analysis techniques including modularization, graphical representation, Failure Mode Effects Simulation (FMES), and parallel processing. These techniques are most useful in modeling large systems. Using modularization, an analyst can create reliability models for individual system components. The modules can be tested separately and then combined to compute the total system reliability. Because a one-to-one relationship can be established between system components and the reliability modules, a graphical user interface may be used to describe the system model. RML was designed to permit message passing between modules. This feature enables reliability modeling based on a run time simulation of the system wide effects of a component's failure modes. The use of failure modes effects simulation enhances the analyst's ability to correctly express system behavior when using the modularization approach to reliability modeling. To alleviate the computation bottleneck often found in large reliability models, REST was designed to take advantage of parallel processing on hypercube processors

Efficacy of needle-placement technique in radiofrequency ablation for treatment of lumbar facet arthropathy.

BACKGROUND:Many studies have assessed the efficacy of radiofrequency ablation to denervate the facet joint as an interventional means of treating axial low-back pain. In these studies, varying procedural techniques were utilized to ablate the nerves that innervate the facet joints. To date, no comparison studies have been performed to suggest superiority of one technique or even compare the prevalence of side effects and complications. MATERIALS AND METHODS:A retrospective chart review was performed on patients who underwent a lumbar facet denervation procedure. Each patient's chart was analyzed for treatment technique (early versus advanced Australian), preprocedural visual numeric scale (VNS) score, postprocedural VNS score, duration of pain relief, and complications. RESULTS:Pre- and postprocedural VNS scores and change in VNS score between the two groups showed no significant differences. Patient-reported benefit and duration of relief was greater in the advanced Australian technique group (P=0.012 and 0.022, respectively). The advanced Australian technique group demonstrated a significantly greater median duration of relief (4 months versus 1.5 months, P=0.022). Male sex and no pain-medication use at baseline were associated with decreased postablation VNS scores, while increasing age and higher preablation VNS scores were associated with increased postablation VNS scores. Despite increasing age being associated with increased postablation VNS scores, age and the advanced Australian technique were found to confer greater patient self-reported treatment benefit. CONCLUSION:The advanced Australian technique provides a significant benefit over the early Australian technique for the treatment of lumbar facet pain, both in magnitude and duration of pain relief

Intravenous versus oral iron supplementation for correction of post-transplant anaemia in renal transplant patients

Background Post-transplant anaemia remains a common problem after kidney transplantation, with an incidence ranging from nearly 80% at day 0 to about 25% at 1 year. It has been associated with poor graft outcome, and recently has also been shown to be associated with increased mortality. Our transplant unit routinely administers oral iron supplements to renal transplant recipients but this is frequently accompanied by side effects, mainly gastrointestinal intolerance. Intravenous iron is frequently administered to dialysis patients and we sought to investigate this mode of administration in transplant recipients after noticing less anaemia in several patients who had received intravenous iron just prior to being called in for transplantation. Methods This study is a single-centre, prospective, open-label, randomised, controlled trial of oral versus intravenous iron supplements in renal transplant recipients and aims to recruit approximately 100 patients over a 12-month period. Patients will be randomised to receive a single dose of 500 mg iron polymaltose (intravenous iron group) or 2 ferrous sulphate slow-release tablets daily (oral iron group). The primary outcome is time to normalisation of haemoglobin post-transplant. Prospective power calculations have indicated that a minimum of 48 patients in each group would have to be followed up for 3 months in order to have a 90% probability of detecting a halving of the time to correction of haemoglobin levels to ≥110 g/l in iron-treated patients, assuming an α of 0.05. All eligible adult patients undergoing renal transplantation at the Princess Alexandra Hospital will be offered participation in the trial. Exclusion criteria will include iron overload (transferrin saturation >50% or ferritin >800 μg/l), or previous intolerance of either oral or intravenous iron supplements. Discussion If the trial shows a reduction in the time to correction of anaemia with intravenous iron or less side effects than oral iron, then intravenous iron may become the standard of treatment in this patient group

Pain control following inguinal herniorrhaphy: current perspectives

Inguinal hernia repair is one of the most common surgeries performed worldwide. With the success of modern hernia repair techniques, recurrence rates have significantly declined, with a lower incidence than the development of chronic postherniorrhaphy inguinal pain (CPIP). The avoidance of CPIP is arguably the most important clinical outcome and has the greatest impact on patient satisfaction, health care utilization, societal cost, and quality of life. The etiology of CPIP is multifactorial, with overlapping neuropathic and nociceptive components contributing to this complex syndrome. Treatment is often challenging, and no definitive treatment algorithm exists. Multidisciplinary management of this complex problem improves outcomes, as treatment must be individualized. Current medical, pharmacologic, interventional, and surgical management strategies are reviewed

Using intervention mapping and behavior change techniques to develop a digital intervention for self-management in stroke: Development study

BACKGROUND: Digital therapeutics, such as interventions provided via smartphones or the internet, have been proposed as promising solutions to support self-management in persons with chronic conditions. However, the evidence supporting self-management interventions through technology in stroke is scarce, and the intervention development processes are often not well described, creating challenges in explaining why and how the intervention would work. OBJECTIVE: This study describes a specific use case of using intervention mapping (IM) and the taxonomy of behavior change techniques (BCTs) in designing a digital intervention to manage chronic symptoms and support daily life participation in people after stroke. IM is an implementation science framework used to bridge the gap between theories and practice to ensure that the intervention can be implemented in real-world settings. The taxonomy of BCTs consists of a set of active ingredients designed to change self-management behaviors. METHODS: We used the first 4 steps of the IM process to develop a technology-supported self-management intervention, interactive Self-Management Augmented by Rehabilitation Technologies (iSMART), adapted from a face-to-face stroke-focused psychoeducation program. Planning group members were involved in adapting the intervention. They also completed 3 implementation measures to assess the acceptability, appropriateness, and feasibility of iSMART. RESULTS: In step 1, we completed a needs assessment consisting of assembling a planning group to codevelop the intervention, conducting telephone surveys of people after stroke (n=125) to identify service needs, and performing a systematic review of randomized controlled trials to examine evidence of the effectiveness of digital self-management interventions to improve patient outcomes. We identified activity scheduling, symptom management, stroke prevention, access to care resources, and cognitive enhancement training as key service needs after a stroke. The review suggested that digital self-management interventions, especially those using cognitive behavioral theory, effectively reduce depression, anxiety, and fatigue and enhance self-efficacy in neurological disorders. Step 2 identified key determinants, objectives, and strategies for self-management in iSMART, including knowledge, behavioral regulation, skills, self-efficacy, motivation, negative and positive affect, and social and environmental support. In step 3, we generated the intervention components underpinned by appropriate BCTs. In step 4, we developed iSMART with the planning group members. Especially, iSMART simplified the original psychoeducation program and added 2 new components: SMS text messaging and behavioral coaching, intending to increase the uptake by people after stroke. iSMART was found to be acceptable (mean score 4.63, SD 0.38 out of 5), appropriate (mean score 4.63, SD 0.38 out of 5), and feasible (mean score 4.58, SD 0.34 out of 5). CONCLUSIONS: We describe a detailed example of using IM and the taxonomy of BCTs for designing and developing a digital intervention to support people after stroke in managing chronic symptoms and maintaining active participation in daily life

Improvements in forecasting intense rainfall: results from the FRANC (forecasting rainfall exploiting new data assimilation techniques and novel observations of convection) project

The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project’s achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model. We present results indicating a link between the spatial predictability of convection and convective regimes, with potential to allow improved forecast interpretation. The research was carried out as a partnership between University researchers and the Met Office (UK). We discuss the benefits of this approach and the impact of our research, which has helped to improve operational forecasts for convective rainfall event

Parallel symbolic state-space exploration is difficult, but what is the alternative?

State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal